| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: label |
| dtype: |
| class_label: |
| names: |
| '0': Alternaria_Boltch |
| '1': Brown_Spot |
| '2': Grey_spot |
| '3': Mosaic |
| '4': Rust |
| splits: |
| - name: train |
| num_bytes: 574217137 |
| num_examples: 26377 |
| download_size: 886186963 |
| dataset_size: 574217137 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| size_categories: |
| - 10K<n<100K |
| --- |
| # Pepper Disease Pest Classification |
|
|
| The dataset comprises real RGB images of pepper plants in field environments, collected over two years (2022-2023) in Shouguang City, China. Captured using a fixed platform setup, the images provide consistent visual data for computer vision research focused on agricultural disease detection in pepper crops. The dataset contains 26,377 images across 5 classes: Alternaria_Boltch, Brown_Spot, Grey_spot, Mosaic, Rust. |
| Images per class: |
| - Alternaria_Boltch: 5,343 |
| - Brown_Spot: 5,655 |
| - Grey_spot: 4,810 |
| - Mosaic: 4,875 |
| - Rust: 5,694 |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wang2025advanced, |
| title={An advanced deep learning method for pepper diseases and pests detection}, |
| author={Wang, Xuewei and Liu, Jun and Chen, Qian}, |
| journal={Plant Methods}, |
| volume={21}, |
| pages={70}, |
| year={2025}, |
| publisher={BioMed Central} |
| } |
| ``` |
|
|
| liu, jun (2024), “vegetable disease”, Mendeley Data, V1, doi: 10.17632/tg3z7xxkdb.1 |
|
|
| *This dataset was reformatted from its original format to match HuggingFace standards.* |
|
|
